Alcohol consumption, depressive symptoms, and the incidence of diabetes‐related complications
Bibliographic record
Abstract
BACKGROUND: Heavy alcohol consumption in individuals with type 2 diabetes mellitus (T2DM) is related to increased risks of diabetes-related micro- and macrovascular complications. Depressive symptoms may be relevant to this relationship, because high depressive symptoms are associated with an increased risk of complications. This study investigated whether the interaction between depressive symptoms and alcohol frequency was positively related to the development of neuropathy, retinopathy, nephropathy, and coronary artery disease (CAD), such that those with high depressive symptoms and high alcohol frequency will be at increased risk of complications. METHODS: Data were from five waves of the Evaluation of Diabetes Treatment annual survey including 1413 adults with T2DM in Quebec. Data on alcohol frequency (number of drinking occasions), depressive symptoms, and complications were collected annually. The development of each complication was investigated using multiple logistic regression analysis with generalized estimating equations. RESULTS: After adjusting for sociodemographic, lifestyle, and diabetes-related covariates, the interaction between alcohol frequency and depressive symptoms was positively related to the incidence of neuropathy and CAD, such that those with high depressive symptoms who drank the most frequently had the highest risk of neuropathy (odds ratio [OR] 1.02, 95% confidence interval [CI] 1.00-1.04; P = 0.04) and CAD (OR 1.02, 95% CI 1.00-1.04; P = 0.04). This interaction was not significantly related to retinopathy or nephropathy. CONCLUSION: Individuals with high depressive symptoms and high alcohol frequency may have a particularly high risk of neuropathy and CAD. Future prevention efforts should examine both alcohol frequency and depressive symptoms when evaluating the risk of complications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".